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Semantic labelling and instance segmentation are two tasks that require particularly costly annotations.
What energy functions can be minimized via graph cuts?
V. Kolmogorov and R. Zabih · 2004
Earlier work this paper cites.
Grabcut: Interactive foreground extraction using iterated graph cuts
C. Rother, V. Kolmogorov, and A. Blake · 2004
Earlier work this paper cites.
Decomposing a scene into geometric and semantically consistent regions
S. Gould, R. Fulton, and D. Koller · 2009
Earlier work this paper cites.
Image segmentation with a bounding box prior
V. Lempitsky, P. Kohli, C. Rother, and T. Sharp · 2009
Earlier work this paper cites.
Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context
J. Shotton, J. Winn, C. Rother, and A. Criminisi · 2009
Earlier work this paper cites.
Contour detection and hierarchical image segmentation
P. Arbeláez, M. Maire, C. Fowlkes, and J. Malik · 2011
Earlier work this paper cites.
Semantic contours from inverse detectors
B. Hariharan, P. Arbeláez, L. Bourdev, S. Maji, and J. Malik · 2011
Earlier work this paper cites.
Efficient inference in fully connected crfs with gaussian edge potentials
P. Krähenbühl and V. Koltun · 2011
Earlier work this paper cites.
Simultaneous detection and segmentation
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Recurrent convolutional neural networks for scene labeling
P. O. Pinheiro and R. Collobert · 2014
Earlier work this paper cites.
J. Barron and B. Poole · 2015
Earlier work this paper cites.
What’s the point: Semantic segmentation with point supervision
A. Bearman, O. Russakovsky, V. Ferrari, and L. Fei-Fei · 2015
Earlier work this paper cites.
Semantic image segmentation with deep convolutional nets and fully connected crfs
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille · 2015
Earlier work this paper cites.
Densecut: Densely connected crfs for realtime grabcut
M. Cheng, V. Prisacariu, S. Zheng, P. Torr, and C. Rother · 2015
Earlier work this paper cites.
Boxsup: Exploiting bounding boxes to supervise convolutional networks for semantic segmentation
J. Dai, K. He, and J. Sun · 2015
Earlier work this paper cites.
The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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Fast R-CNN
R. Girshick · 2015
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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Decoupled deep neural network for semi-supervised semantic segmentation
S. Hong, H. Noh, and B. Han · 2015
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What makes for effective detection proposals?
J. Hosang, R. Benenson, P. Dollár, and B. Schiele · 2015
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Learning to propose objects
P. Krähenbühl and V. Koltun · 2015
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Secrets of grabcut and kernel k-means
M. Tang, I. Ben Ayed, D. Marin, and Y. Boykov · 2015
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Superdifferential cuts for binary energies
T. Taniai, Y. Matsushita, and T. Naemura · 2015
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Stc: A simple to complex framework for weakly-supervised semantic segmentation
Y. Wei, X. Liang, Y. Chen, X. Shen, M.-M. Cheng, Y. Zhao, and S. Yan · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Learning to segment under various forms of weak supervision
J. Xu, A. Schwing, and R. Urtasun · 2015
Later among the works it cites.
Loosecut: Interactive image segmentation with loosely bounded boxes
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Weakly- and semi-supervised learning of a dcnn for semantic image segmentation
G. Papandreou, L. Chen, K. Murphy, , and A. L. Yuille · 2015
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Constrained convolutional neural networks for weakly supervised segmentation
D. Pathak, P. Kraehenbuehl, and T. Darrell · 2015
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Fully convolutional multi-class multiple instance learning
D. Pathak, E. Shelhamer, J. Long, and T. Darrell · 2015
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From image-level to pixel-level labeling with convolutional network
P. Pinheiro and R. Collobert · 2015
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Learning to segment object candidates
P. O. Pinheiro, R. Collobert, and P. Dollár · 2015
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H. Yu, Y. Zhou, H. Qian, M. Xian, Y. Lin, D. Guo, K. Zheng, K. Abdelfatah, and S. Wang · 2015
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Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. Torr · 2015
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Human pose estimation with iterative error feedback
J. Carreira, P. Agrawal, K. Fragkiadaki, and J. Malik · 2016
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L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2016
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Weakly supervised object boundaries
A. Khoreva, R. Benenson, M. Omran, M. Hein, and B. Schiele · 2016
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Pushing the boundaries of boundary detection using deep learning
I. Kokkinos · 2016
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Scribblesup: Scribble-supervised convolutional networks for semantic segmentation
D. Lin, J. Dai, J. Jia, K. He, and J. Sun · 2016
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Efficient piecewise training of deep structured models for semantic segmentation
G. Lin, C. Shen, A. van dan Hengel, and I. Reid · 2016
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Learning to refine object segments
P. Pinheiro, T.-Y. Lin, R. Collobert, and P. Dollár · 2016
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Deep interactive object selection
N. Xu, B. Price, S. Cohen, J. Yang, and T. S. Huang · 2016
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Multi-scale context aggregation by dilated convolutions
F. Yu and V. Koltun · 2016
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